System for optimizing software application licenses

Abstract

A system and method for license optimization of a software application in an organization. A usage data of a feature of a software application from one or more disparate sources is received. Subsequently, a usage insight of the feature of the software application is determined based on the usage data. A license optimization recommendation is provided to a user based on the usage insight of the feature of the software application. Further, the license optimization recommendation is provided using a machine learning model. Also, the license optimization recommendation comprises reallocation of a license tier of the software application. The system and method further forecasts a number of licenses required based on the usage insight of the feature of the software application in an organization.

The invention claimed is: 
     
       1. A method for license optimization of a software application in an organization, the method comprising:
 receiving, by a processor, usage data of a plurality of features of the software application from one or more sources, wherein the software application comprises the plurality of features; 
 determining, by the processor, usage insights of the plurality of features based on the usage data of the plurality of features, wherein the usage data is at least one or more of historical usage data, historical license utilization, historical license optimization recommendations, and feedback received to the historical license optimization recommendations for the software application, and wherein the usage insights indicate an average time spent on the feature and a frequency of use of the feature for a duration; 
 providing, by the processor, a license optimization recommendation using a machine learning model, wherein the machine learning model assigns a weight to each feature of the plurality of features based on the usage insights and license tier of the software applications, and wherein the machine learning model uses the usage insights, one of the assigned weight of each feature, and the usage data to recommend license optimization to a user; 
 forecasting, by the processor, a number of licenses required based on the usage insights of the plurality of features of the software application in the organization, and 
 recommending a license optimization to a user, wherein the license optimization recommendation comprises reallocation of a license tier of the software applications. 
 
     
     
       2. The method as claimed in  claim 1 , wherein the machine learning model is recursively trained based on feedback received from the user for the license optimization recommendation. 
     
     
       3. The method as claimed in  claim 1 , further comprises automatically de-provisioning the user from a higher tier software application license to a lower tier software application license based on usage data of a feature of the software application. 
     
     
       4. The method as claimed in  claim 1 , wherein the forecasting is done based on one or more factors including a number of licenses purchased, a license assignment rate, an employee growth rate, an employee attrition rate, and usage data of a feature corresponding to the license tier. 
     
     
       5. The method as claimed in  claim 1 , wherein the usage data of the plurality of features is received from sources comprising one or more of a single sign-on (SSO) integration, an application integration, a browser extension, a desktop agent, and a cloud access security broker (CASB). 
     
     
       6. The method as claimed in  claim 1 , wherein the usage insights further indicate a metric associated with the software application. 
     
     
       7. The method as claimed in  claim 1 , further comprises an automatic license optimization based on the usage insights, wherein the automatic license optimization is performed for at least one of the license tier, a license cost, and the number of licenses of the software application. 
     
     
       8. The method as claimed in  claim 1 , wherein the machine learning model uses the usage insights, the assigned weight of each feature, and the usage data to recommend license optimization to a user. 
     
     
       9. The method as claimed in  claim 1 , wherein the license optimization recommendation further comprises one or more of a number of optimizable licenses, a role-based license optimization, an actual cost saving, a wastage of a number of licenses, potential savings, and actual savings realized. 
     
     
       10. The method as claimed in  claim 1 , further comprises grouping similar software applications for the license optimization of the software application. 
     
     
       11. The method as claimed in  claim 1 , wherein the license optimization comprises one or more of:
 determining, by the processor, a number of unused licenses of the software application; 
 determining, by the processor, last use of the software application; 
 determining, by the processor usage data of a feature of the software application; and 
 determining, by the processor, a metric associated with the usage data of the feature software application. 
 
     
     
       12. The method as claimed in  claim 1 , provides application license optimization recommendations on a graphical user interface (GUI) to take actions for license reallocations. 
     
     
       13. The method of  claim 1 , wherein the machine learning model further assigns a weight to the one or more sources. 
     
     
       14. A system for license optimization of a software application in an organization, the system comprising:
 a memory; and 
 a processor coupled to the memory, wherein the processor is configured to execute program instructions stored in the memory for 
 receiving usage data of a plurality of features of the software application from one or more sources, wherein the software application comprises the plurality of features; 
 determining usage insights of the plurality of features of the software application based on the usage data of the plurality of features, wherein the usage data is at least one or more of historical usage data, historical license utilization, historical license optimization recommendations, and feedback received to the historical license optimization recommendations for the software application, and wherein the usage insights indicate an average time spent on the feature and a frequency of use of the feature for a duration; 
 providing a license optimization recommendation using a machine learning model, wherein the machine learning model assigns a weight to each feature of the plurality of features based on the usage insights and license tier of the software applications, and wherein the machine learning model uses the usage insights, one of the assigned weight of each feature, and the usage data to recommend license optimization to a user; 
 forecasting a number of licenses required based on the usage insights of the plurality of features of the software application in the organization, and 
 recommending a license optimization to a user, wherein the license optimization recommendation comprises reallocation of a license tier of the software applications. 
 
     
     
       15. The system as claimed in  claim 14 , wherein the machine learning model is recursively trained based on a feedback received from the user for the license optimization recommendation. 
     
     
       16. The system as claimed in  claim 14 , further comprises automatically de-provisioning a user from a higher tier software application license to a lower tier software application license based on the usage data of the plurality of features of the software application. 
     
     
       17. The system of  claim 14 , wherein the machine learning model further assigns a weight to the one or more sources.